The composition of a material can be analyzed based on the characteristics of the gamma rays detected by a gamma ray detector. For example, elements typically emit gamma rays at certain characteristic energies when activated with a suitable source of neutrons during neutron activation. Prompt Gamma Neutron Activation Analysis (PGNAA) and Neutron Inelastic Scattering (NIS) techniques have been used for measuring elemental composition in bulk samples. These techniques can produce high energy or highly penetrating gamma rays, which can allow the analysis of large sample volumes.
These techniques have been used to analyze materials in the context of oil well logging. In particular, the carbon/oxygen ratio may provide information about the relative amounts of oil or water in the well. The logging tool generally includes a fast neutron source and a radiation detector spaced apart from the source. The fast neutrons originating from the source collide with formation elements. These collisions often result in the emission of inelastic gamma rays and, subsequently, the slowing down of the neutrons. Neutrons can also be slowed by elastic collisions with elements with small nuclei, such as hydrogen, carbon, and oxygen. Upon slowing down, the neutrons may be captured and another set of gamma rays may be emitted. The resulting gamma rays, either before or after neutron slowing, are detected by the radiation detectors and the resulting spectra are analyzed to obtain information about the elemental amounts in the formation. Carbon and oxygen generally emit gamma rays ranging from 4.44 to 6.13 MeV, which can result from the interaction of fast neutrons with these elements. Gamma rays ranging from 1.6 to 4.8 MeV can also be detected from carbon and oxygen as a result of the capture of primarily thermal neutrons by these elements.
The gamma ray detectors used in a logging tool are constrained in size because of the relatively small size of a borehole. The resulting spectrum may have a low signal-to-noise ratio, and therefore, the data may have poor statistical significance and be difficult to analyze.